Mathematical Models Supporting Control of COVID-19

Deng, Bin; Yan, Ning; Xu, Jingwen; Rui, Jia; Lin, Shengnan; Zhao, Zeyu; Yu, Shanshan; Guo, Yichao; Luo, Li; Chen, Tianmu; Li, Qun

doi:10.46234/ccdcw2022.186

Cited by 8 publications

(3 citation statements)

References 73 publications

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“…The COVID-19 pandemic prompted accelerated development in theoretical epidemiology, with various models predicting trends, simulating transmission, and evaluating control measures to inform government decision-making (3)(4)(25)(26)(27)(28)(29)(30). Subsequently, theoretical epidemiology has focused on refining models and addressing previous limitations, such as real-time database optimization and dynamic modeling to monitor disease progress (31)(32). like autoregressive integrated moving average model (ARIMA), Monte Carlo algorithmic models, gray theory models, neural network models, and related derivatives (33).…”

Section: A Brief History Of the Development Of Theoretical Epidemiologymentioning

confidence: 99%

“…“Data-driven” models encompass various approaches to investigate the connection between disease occurrence and time. These methods involve temporal regression models, control charts, time series models like autoregressive integrated moving average model (ARIMA), Monte Carlo algorithmic models, gray theory models, neural network models, and related derivatives ( 33 ).…”

Section: Overview Of Common Types Of Mathematical Models In Theoretic...mentioning

confidence: 99%

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Theoretical Epidemiology Needs Urgent Attention in China

Wang,

Zhao,

Rui

et al. 2024

China CDC Weekly

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The mathematical method to which theoretical epidemiology belongs is one of the three major methodologies in epidemiology. It is of great value in diagnosing infectious disease epidemic trends and evaluating the effectiveness of prevention and control measures. This paper aims to summarize the brief history of the development of theoretical epidemiology, common types of mathematical models, and key steps to develop a mathematical model. It also provides some thoughts and perspectives on the development and application of theoretical epidemiology in China. OVERVIEW OF COMMON TYPES OF MATHEMATICAL MODELS IN THEORETICAL EPIDEMIOLOGYUpon reviewing the literature, we identified two main types of mathematical models in theoretical epidemiology: "data-driven" models and "mechanismdriven" models."Data-driven" models encompass various approaches to investigate the connection between disease occurrence and time. These methods involve temporal regression models, control charts, time series models China CDC Weekly Chinese Center for Disease Control and Prevention CCDC Weekly / Vol. 6 / No.

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Section: A Brief History Of the Development Of Theoretical Epidemiologymentioning

confidence: 99%

Section: Overview Of Common Types Of Mathematical Models In Theoretic...mentioning

confidence: 99%

Theoretical Epidemiology Needs Urgent Attention in China

Wang,

Zhao,

Rui

et al. 2024

China CDC Weekly

Self Cite

View full text Add to dashboard Cite

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“…Mathematical models can predict how infectious diseases progress to have the likely outcome of an epidemic and help to provide scientific information for public health interventions (26,46,47). Since the outbreak of the COVID-19 pandemic, mathematical models have become a crucial and important approach to accurately understand the transmission characteristics and mechanisms in controlling COVID-19 (48), which usually include time series models (e.g., generalized additive models, autoregressive integrated moving average models, and artificial neural network models) (49)(50)(51), and dynamic models (e.g., ODE: ordinary differential equation models, PDE: partial differential equation models and statistical equation models) (52)(53)(54)(55)(56)(57).…”

Section: Mathematical Models Related To the Simulation And Prediction...mentioning

confidence: 99%

Dynamic variations in and prediction of COVID-19 with omicron in the four first-tier cities of mainland China, Hong Kong, and Singapore

Ni,

Sun,

et al. 2023

Front. Public Health

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BackgroundThe COVID-19 pandemic, which began in late 2019, has resulted in the devastating collapse of the social economy and more than 10 million deaths worldwide. A recent study suggests that the pattern of COVID-19 cases will resemble a mini-wave rather than a seasonal surge. In general, COVID-19 has more severe impacts on cities than on rural areas, especially in cities with high population density.MethodsIn this study, the background situation of COVID-19 transmission is discussed, including the population number and population density. Moreover, a widely used time series autoregressive integrated moving average (ARIMA) model is applied to simulate and forecast the COVID-19 variations in the six cities. We comprehensively analyze the dynamic variations in COVID-19 in the four first-tier cities of mainland China (BJ: Beijing, SH: Shanghai, GZ: Guangzhou and SZ: Shenzhen), Hong Kong (HK), China and Singapore (SG) from 2020 to 2022.ResultsThe major results show that the six cities have their own temporal characteristics, which are determined by the different control and prevention measures. The four first-tier cities of mainland China (i.e., BJ, SH, GZ, and SZ) have similar variations with one wave because of their identical “Dynamic COVID-19 Zero” strategy and strict Non-Pharmaceutical Interventions (NPIs). HK and SG have multiple waves primarily caused by the input cases. The ARIMA model has the ability to provide an accurate forecast of the COVID-19 pandemic trend for the six cities, which could provide a useful approach for predicting the short-term variations in infectious diseases.Accurate forecasting has significant value for implementing reasonable control and prevention measures.ConclusionsOur main conclusions show that control and prevention measures should be dynamically adjusted and organically integrated for the COVID-19 pandemic. Moreover, the mathematical models are proven again to provide an important scientific basis for disease control.

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